Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "159" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460009 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.402337 | 28.058208 | -1.157088 | -0.555641 | -0.370389 | 2.058987 | -0.291886 | 0.565805 | 0.5725 | 0.4687 | 0.3243 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.840760 | 35.105396 | -1.623795 | -0.630179 | -0.805760 | 2.006933 | -0.720644 | 0.434725 | 0.6148 | 0.5292 | 0.2869 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.640833 | 24.973713 | -1.171098 | -0.580437 | -1.007901 | 2.574406 | -0.107475 | 2.203997 | 0.5789 | 0.4993 | 0.3180 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 98.91% | 99.16% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.2629 | 0.2663 | 0.2062 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.381368 | 24.558610 | -1.060495 | -0.429527 | -0.838076 | 2.041503 | -0.530124 | 0.129768 | 0.5709 | 0.4604 | 0.3403 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.569638 | 27.208682 | -0.856980 | -0.398776 | -0.929894 | 2.187918 | 0.028276 | 1.847463 | 0.5961 | 0.4849 | 0.3519 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.519726 | 28.420830 | -1.458906 | -0.636073 | -0.369346 | 3.234198 | 0.161502 | 23.223973 | 0.5754 | 0.4817 | 0.3503 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.405009 | 25.488056 | -1.316010 | -0.594154 | -0.315255 | 4.734707 | 0.952347 | 19.301957 | 0.5683 | 0.4905 | 0.3522 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 274.517486 | 274.599023 | inf | inf | 3669.079748 | 3670.351297 | 6753.038038 | 6733.078425 | nan | nan | nan | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.697103 | 23.482901 | -1.342536 | -0.778508 | -0.089585 | 5.794077 | -0.461058 | 42.586377 | 0.5842 | 0.5306 | 0.3585 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.627427 | 11.690909 | -1.319678 | -1.009496 | -0.310933 | 5.263809 | -0.479633 | 21.973858 | 0.5803 | 0.5655 | 0.3635 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.413064 | 2.929442 | -1.005720 | -1.034373 | -0.218721 | 0.977811 | -0.290945 | 12.443759 | 0.5737 | 0.5916 | 0.3704 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.519311 | 12.161177 | -1.450684 | -1.156543 | 0.149521 | 4.409362 | -0.336361 | 77.538776 | 0.5773 | 0.5689 | 0.3512 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.424738 | 27.335078 | -1.383706 | -0.571282 | -0.832162 | 2.832640 | -0.150900 | 6.424853 | 0.5870 | 0.4992 | 0.3405 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.707015 | 29.452730 | -1.492274 | -0.716382 | -0.385559 | 5.610013 | -0.074228 | 20.203353 | 0.6049 | 0.5409 | 0.3115 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.358450 | 31.408510 | -1.373274 | -0.579426 | -0.664261 | 3.612942 | -0.534838 | 35.366233 | 0.5870 | 0.4838 | 0.3494 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.565746 | 26.432240 | -1.323198 | -0.656373 | -1.259579 | 3.814309 | -0.655422 | 0.027284 | 0.6020 | 0.5256 | 0.3332 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.412718 | 27.084041 | -1.392037 | -0.626627 | -0.286196 | 6.315442 | -0.329396 | 20.044162 | 0.6167 | 0.5528 | 0.2889 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.096404 | 17.651612 | -1.032450 | -0.650539 | -1.113392 | 2.584517 | -0.961396 | 2.948816 | 0.6604 | 0.6006 | 0.2797 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.417341 | 24.925296 | -1.485741 | -0.713067 | 0.295525 | 6.212868 | -0.478876 | 66.714918 | 0.5838 | 0.5040 | 0.3478 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.341164 | 26.543002 | -1.553205 | -0.750840 | -0.830844 | 5.099357 | -0.762942 | 3.326785 | 0.6276 | 0.5372 | 0.2868 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.424194 | 20.755041 | -1.440371 | -0.943238 | 0.028616 | 5.092987 | -0.762241 | 43.558260 | 0.5764 | 0.5292 | 0.3524 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.386365 | 26.138935 | -1.443254 | -0.789886 | 0.096382 | 2.954087 | -0.831292 | 41.511885 | 0.5773 | 0.5015 | 0.3549 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.394564 | 26.992591 | -1.492316 | -0.716329 | -0.503915 | 6.860818 | -0.756598 | 25.708444 | 0.5432 | 0.4591 | 0.3126 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.450124 | 27.139377 | -1.573869 | -0.768763 | -0.185379 | 4.191466 | -0.454397 | 32.748403 | 0.5815 | 0.4973 | 0.3465 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 28.058208 | 0.402337 | 28.058208 | -1.157088 | -0.555641 | -0.370389 | 2.058987 | -0.291886 | 0.565805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 35.105396 | 35.105396 | 0.840760 | -0.630179 | -1.623795 | 2.006933 | -0.805760 | 0.434725 | -0.720644 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 24.973713 | 0.640833 | 24.973713 | -1.171098 | -0.580437 | -1.007901 | 2.574406 | -0.107475 | 2.203997 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 24.558610 | 0.381368 | 24.558610 | -1.060495 | -0.429527 | -0.838076 | 2.041503 | -0.530124 | 0.129768 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 27.208682 | 0.569638 | 27.208682 | -0.856980 | -0.398776 | -0.929894 | 2.187918 | 0.028276 | 1.847463 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 28.420830 | 0.519726 | 28.420830 | -1.458906 | -0.636073 | -0.369346 | 3.234198 | 0.161502 | 23.223973 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 25.488056 | 0.405009 | 25.488056 | -1.316010 | -0.594154 | -0.315255 | 4.734707 | 0.952347 | 19.301957 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | ee Power | inf | 274.517486 | 274.599023 | inf | inf | 3669.079748 | 3670.351297 | 6753.038038 | 6733.078425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 42.586377 | 0.697103 | 23.482901 | -1.342536 | -0.778508 | -0.089585 | 5.794077 | -0.461058 | 42.586377 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 21.973858 | 11.690909 | 0.627427 | -1.009496 | -1.319678 | 5.263809 | -0.310933 | 21.973858 | -0.479633 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 12.443759 | 2.929442 | 0.413064 | -1.034373 | -1.005720 | 0.977811 | -0.218721 | 12.443759 | -0.290945 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 77.538776 | 12.161177 | 0.519311 | -1.156543 | -1.450684 | 4.409362 | 0.149521 | 77.538776 | -0.336361 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 27.335078 | 0.424738 | 27.335078 | -1.383706 | -0.571282 | -0.832162 | 2.832640 | -0.150900 | 6.424853 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 29.452730 | 29.452730 | 0.707015 | -0.716382 | -1.492274 | 5.610013 | -0.385559 | 20.203353 | -0.074228 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 35.366233 | 31.408510 | 0.358450 | -0.579426 | -1.373274 | 3.612942 | -0.664261 | 35.366233 | -0.534838 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 26.432240 | 0.565746 | 26.432240 | -1.323198 | -0.656373 | -1.259579 | 3.814309 | -0.655422 | 0.027284 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 27.084041 | 0.412718 | 27.084041 | -1.392037 | -0.626627 | -0.286196 | 6.315442 | -0.329396 | 20.044162 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 17.651612 | 1.096404 | 17.651612 | -1.032450 | -0.650539 | -1.113392 | 2.584517 | -0.961396 | 2.948816 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 66.714918 | 24.925296 | 0.417341 | -0.713067 | -1.485741 | 6.212868 | 0.295525 | 66.714918 | -0.478876 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 26.543002 | 26.543002 | 0.341164 | -0.750840 | -1.553205 | 5.099357 | -0.830844 | 3.326785 | -0.762942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 43.558260 | 0.424194 | 20.755041 | -1.440371 | -0.943238 | 0.028616 | 5.092987 | -0.762241 | 43.558260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 41.511885 | 26.138935 | 0.386365 | -0.789886 | -1.443254 | 2.954087 | 0.096382 | 41.511885 | -0.831292 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Shape | 26.992591 | 0.394564 | 26.992591 | -1.492316 | -0.716329 | -0.503915 | 6.860818 | -0.756598 | 25.708444 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | N13 | RF_maintenance | nn Temporal Discontinuties | 32.748403 | 27.139377 | 0.450124 | -0.768763 | -1.573869 | 4.191466 | -0.185379 | 32.748403 | -0.454397 |